The use of the gait profile score and gait variable score in individuals with Duchenne Muscular Dystrophy

Therapeutic gait interventions for individuals with Duchenne Muscular Dystrophy (DMD) should be based on understanding how movement of the individual is affected and whether different clusters of individuals, determined by clinical severity, differ. Gait indexes have been developed to synthesize the data provided by the three dimensional (3D) gait analysis such as the Gait Deviation Index (GDI) and the Gait Profile Score (GPS) where the gait variable score (GVS) can be calculated. The objective this study was to evaluate the potential use of the GDI and GPS and MAP using data from 3D gait analysis of DMD patients. The dimension 1 score of the Motor Function Measurement defined the groups that composed the cluster analysis. Twenty patients with DMD composed 2 groups according to the cluster analysis (Cluster 1, n=10; Cluster 2, n=10). Three-dimensional gait analysis was conducted where GDI, GPS and GVS (pelvic tilt/obliquity; hip flexion-extension/ adduction-abduction/ rotation; knee flexion-extension; ankle dorsiflexion-plantarflexion, foot progression angle) were calculated. Cluster 1 group presented lower hip flexion-extension and lower pelvic obliquity when compared with Cluster 2 group (p<0.05). There was no difference between groups for GDI, GPS total and maximum isometric muscle strength of the lower limbs (p>0.05). This study showed that GVS could detect alterations on the parameters obtained using three-dimensional gait analysis for those DMD patients separated according to motor function regarding pelvic and hip kinematic patterns. The rehabilitation of patients with DMD is recommended from the early stages of the disease (as Cluster 1, with > MFM) with the hip joint being the therapeutic target

Nanotechnology in hormone replacement therapy: Safe and efficacy of transdermal estriol and estradiol nanoparticles after 5 Years Follow-Up Study

This study aimed to evaluate the safety and efficacy of a novel protocol of transdermal Hormone Replacement Therapy (HRT) based on a nanostructured formulation of Estriol (0.1 %) + Estradiol (0.25 %) restoring serum levels and relieving menopausal symptoms. We evaluated 122 women with mean age of 56.88 (± 6.27) as part a longitudinal prospective study on post-menopausal women with natural menopause, received in the right forearm a transdermal formulation of (EE) daily for 60 months. Clinical parameters including the degree of satisfaction with symptomatic relief, serum concentrations of estradiol, weight, blood pressure, and bilateral mammography BI-RADS were compared between the baseline and five years after treatment. New evidence regarding this HRT protocol was assessed. The transdermal nanoformulation estradiol improved clinical parameters. Satisfaction with treatment was 92 %. Serum concentrations of estradiol changed significantly after treatment (p 0.05) over the years. No vaginal bleeding was observed. Bilateral mammography assessment of the breasts following 60 months of HRT with bioidentical estradiol treatment found normal results in all women. This paper shows for the first time the effectiveness of a nanostructured transdermal formulation enhancer on the delivery of estradiol and estriol measured in vivo using Raman Confocal Spectroscopy. The Nanostructured formulation is safe and effective in reestablishing estradiol serum levels and relieving menopausal symptoms. The nanoformulation may serve as a good choice for hormone replacement therapy to protect against other post-menopausal symptoms.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost